Computer Vision: Face Recognition Quick Starter in Python [Video]

By Abhilash Nelson
  • Instant online access to over 7,500+ books and videos
  • Constantly updated with 100+ new titles each month
  • Breadth and depth in over 1,000+ technologies
  1. Introduction to Face Recognition

About this video

Face detection and face recognition are the most popular aspects in computer vision. They are widely used by governments and organizations for surveillance and policing. Moreover, they also have applications in our day-to-day life such as face unlocking mobile phones.

This course will help you delve into face recognition using Python without having to deal with all the complexities and mathematics associated with the deep learning process.

You will start with an introduction to face detection and face recognition technology. After this, you’ll get the system ready for Python coding by downloading and installing the Anaconda package and other dependencies and libraries that are required such as dlib and OpenCV. You’ll then write Python code to detect faces from a given image and extract the faces as separate images. Next, you’ll focus on face detection by streaming a real-time video from the webcam. The course will also guide you on how to customize the face detection program to blur the detected faces dynamically from the webcam video stream. Moving ahead, you’ll go on to learn facial expression recognition and age and gender prediction using a pre-trained deep learning model. Later, you’ll progress to writing Python code for face recognition, which will help identify the faces that are already detected. You’ll use static images as well as live streaming video from the computer’s webcam to recognize the detected faces with their names. The course then explores the concept of face distance, teaching you how to convert the face distance value to face matching percentage using simple mathematics. Finally, you’ll be able to tweak the face landmark points used for face detection. You’ll draw a line joining the face landmark points to visualize the points in the face which the computer used for evaluation. Taking the landmark points customization to the next level, you’ll create custom face make-up for the face image.

By the end of this course, you’ll be well-versed with face recognition and detection and be able to apply your skills in the real world. All the codes and supporting files for this course will be available at-

Publication date:
July 2020
3 hours 51 minutes

About the Author

  • Abhilash Nelson

    Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web developer and application developer with more than eight years of IT experience in designing, implementing, integrating, testing, and supporting web and mobile applications. He has a master's degree in computer science and engineering. His experience with PHP/Python programming has also helped him contribute to creating server-based Android and iOS client applications. He is currently working as a Senior Solutions Architect, managing his clients’ projects from start to finish to ensure high quality and innovative and functional design.

    Browse publications by this author